In the realm of thermal management, particularly in the energy sector, the quest for efficient heat transfer solutions is unending. Researchers from the Department of Mathematics and Statistics at Hazara University, Mansehra, Pakistan, led by Darvesh Adil, have made a significant stride in this direction. Their recent study, published in ‘Applied Rheology’ (translated to English as ‘Applied Rheology’), delves into the complex interplay of thermal transport in polymer-based ternary radiative Carreau nanofluids under extreme shear rates over a bullet-shaped surface. This isn’t just academic curiosity; it’s a potential game-changer for industries relying on fluid-based thermal management systems.
The study focuses on the heat transfer mechanism in the flow of ternary hybrid nanofluids over a bullet-shaped geometry. This geometry is particularly relevant in aerospace, automotive, and energy sectors, where efficient heat dissipation is crucial. The researchers integrated the infinite shear rate viscosity-based model of Carreau to explore enhanced heat transport capabilities. “Our goal was to develop an advanced predictive model that can accurately capture thermal predictions in ternary hybrid nanofluids under varying shear rate conditions,” Adil explains. This is no small feat, given the complexity of the variables involved—viscous dissipation, non-uniform heat sink source, thermal radiation, and infinite shear rate viscosity.
The research team employed artificial neural networks (ANNs) to simulate and analyze how these fluids respond to the combined effects of these variables. The physical model generated a set of partial differential equations, which were then converted into ordinary differential equations (ODEs) using similarity transformations. These ODEs were numerically solved using the bvp4c method, and the solutions were compiled into a dataset to train the ANN. The neural network was designed to predict advanced solutions, providing insights that could revolutionize thermal management strategies.
The findings are compelling. The study revealed that the velocity magnitude increases with the stretching ratio and infinite shear rate parameter but decreases with the location parameter and velocity slip parameter. Conversely, the temperature profile decreased with an increase in the radiation parameter and Eckert numbers, showing an opposite trend for heat generation number and magnetic parameter. Notably, the rate of temperature increment is highest in ternary hybrid nanofluids compared to nanofluids and hybrid nanofluids.
So, what does this mean for the energy sector? Efficient thermal management is critical for the performance and longevity of energy systems. By understanding and predicting thermal transport in these complex fluids, industries can design more efficient cooling systems, reduce energy losses, and enhance the overall performance of their equipment. This research could pave the way for innovative cooling solutions in power plants, data centers, and even electric vehicles, where thermal management is a significant challenge.
The implications of this research are vast. As Adil puts it, “The integration of advanced computational methods like ANNs with traditional fluid dynamics can unlock new possibilities in thermal management.” This study not only advances our theoretical understanding but also provides practical tools for engineers and scientists to develop more efficient and sustainable thermal management systems.
The research published in ‘Applied Rheology’ marks a significant step forward in the field of thermal transport prediction. As industries continue to push the boundaries of efficiency and sustainability, studies like these will be instrumental in shaping the future of energy and thermal management technologies.
